Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_resources.txt

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paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_result.json

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paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/final_panel_membership.csv

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paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/finalize_audit.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+"""Finalize receipts and donor-aware descriptive summaries; never change ranking."""
+from __future__ import annotations
+
+import gzip
+import json
+from pathlib import Path
+
+import pandas as pd
+from audit_training_ilc import ROOT, PLAN, KEY, receipt, dump
+
+v = pd.read_parquet(ROOT / 'training_ilc_marker_counts.parquet')
+t = pd.read_csv(ROOT / 'locked_target_genes.csv')
+now = pd.read_csv(PLAN / 'ilc_targeted_audit_genes.csv')
+assert t[['gene_id', 'gene_symbol', 'raw_panel_position']].equals(now[['gene_id', 'gene_symbol', 'raw_panel_position']])
+assert t.in_final6k.equals(now.in_final6k)
+membership = now[['gene_id', 'in_final6k', 'in_7k', 'in_8k']]
+for size in ['7k', '8k']:
+    panel = pd.read_csv(PLAN / 'panels' / size / 'selected_genes.csv')
+    assert len(panel) == int(size[:-1])*1000 and not panel.gene_id.duplicated().any()
+    membership.loc[:, 'in_' + size] = membership.gene_id.isin(panel.gene_id)
+membership.to_csv(ROOT / 'final_panel_membership.csv', index=False)
+for name in ['overall', 'by_dataset', 'by_donor']:
+    p = ROOT / f'marker_support_{name}.csv'
+    a = pd.read_csv(p).drop(columns=['in_final6k', 'in_7k', 'in_8k'])
+    a.merge(membership, on='gene_id', validate='many_to_one').to_csv(p, index=False)
+
+geo = Path('/home/sj_server_1/PBISC/paper-release-20260928/manuscript/source_data/corpus/geo/GSE132802_series_matrix.txt.gz')
+fields = {}
+with gzip.open(geo, 'rt') as f:
+    for line in f:
+        if line.startswith('!Sample_'):
+            k, *vals = line.rstrip('\n').split('\t')
+            fields.setdefault(k, []).append([x.strip('"') for x in vals])
+descriptions = [x.strip() for x in fields['!Sample_description'][0]]
+diagnoses = next(x for x in fields['!Sample_characteristics_ch1'] if x[0].startswith('disease diagnosis:'))
+correction = {k: ('healthy volunteer' if d == 'disease diagnosis: Healthy' else 'DRESS patient')
+              for k, d in zip(descriptions, diagnoses)}
+v['corrected_donor_id'] = v.donor_id
+is300b = v.dataset_id.str.startswith('300b_A')
+v.loc[is300b, 'corrected_donor_id'] = v.loc[is300b, 'donor_id'].map(correction)
+assert v.corrected_donor_id.notna().all()
+donor_keys = ['dataset_id', 'corrected_donor_id']
+assert len(v[donor_keys].drop_duplicates()) == 937
+source_donor_map = v[['dataset_id', 'donor_id', 'corrected_donor_id']].drop_duplicates()
+source_donor_map.to_csv(ROOT / 'donor_identity_map.csv', index=False)
+gene_rows = []
+for (ds, dn), d in v.groupby(donor_keys, sort=True):
+    for gid, symbol in zip(t.gene_id, t.gene_symbol):
+        measured = d[gid + '__measured']
+        detected = measured & d[gid].gt(0)
+        eligible = int(measured.sum()); n = int(detected.sum())
+        gene_rows.append({'dataset_id': ds, 'corrected_donor_id': dn, 'gene_id': gid,
+             'gene_symbol': symbol, 'source_labelled_ilc_cells': len(d),
+             'measured_eligible_cells': eligible, 'detected_cells': n,
+             'detection_fraction_among_measured': n/eligible if eligible else None,
+             'total_raw_counts': int(d.loc[measured, gid].sum())})
+pd.DataFrame(gene_rows).to_csv(ROOT / 'marker_support_by_corrected_donor.csv', index=False)
+overall = pd.read_csv(ROOT / 'marker_support_overall.csv')
+for gid in t.gene_id:
+    e = v[v[gid + '__measured']]
+    d = e[e[gid].gt(0)]
+    overall.loc[overall.gene_id.eq(gid), 'measured_corrected_dataset_scoped_donors'] = len(e[donor_keys].drop_duplicates())
+    overall.loc[overall.gene_id.eq(gid), 'detected_corrected_dataset_scoped_donors'] = len(d[donor_keys].drop_duplicates())
+    overall.loc[overall.gene_id.eq(gid), 'measured_datasets'] = e.dataset_id.nunique()
+    overall.loc[overall.gene_id.eq(gid), 'detected_datasets'] = d.dataset_id.nunique()
+overall.to_csv(ROOT / 'marker_support_overall.csv', index=False)
+depth = pd.read_csv(ROOT / 'native_depth_support.csv').rename(columns={'source_donor_ids': 'dataset_scoped_source_donor_ids'})
+depth.loc[depth.dataset_id.eq('ALL'), 'dataset_scoped_source_donor_ids'] = len(v[KEY[:2]].drop_duplicates())
+depth.to_csv(ROOT / 'native_depth_support.csv', index=False)
+dataset = v.groupby('dataset_id').agg(cells=('shard_row', 'size'), samples=('sample_key', 'nunique'),
+              original_donor_ids=('donor_id', 'nunique'), corrected_donors=('corrected_donor_id', 'nunique')).reset_index()
+dataset.to_csv(ROOT / 'selected_dataset_support.csv', index=False)
+verification = json.loads((ROOT / 'verification.json').read_text())
+assert verification['status'] == 'PASS'
+plan = json.loads((ROOT / 'extraction_plan.json').read_text())
+extraction = json.loads((ROOT / 'extraction_result.json').read_text())
+missing6 = overall[~overall.in_final6k]
+result = {'status': 'verified', 'scope': 'Existing source-labelled ILC records in exact registered TRAIN split; no new annotation or model inference',
+          'source_ilc_cells': 19329, 'train_ilc_cells': len(v), 'datasets': v.dataset_id.nunique(),
+          'source_sample_keys': v.sample_key.nunique(), 'source_dataset_scoped_donor_ids': len(v[KEY[:2]].drop_duplicates()),
+          'corrected_dataset_scoped_donor_ids': len(v[donor_keys].drop_duplicates()),
+          'target_raw_loci': len(t), 'target_gene_symbols': t.gene_symbol.nunique(),
+          'missing6k_raw_loci': len(missing6),
+          'missing6k_loci_detected_in_at_least_one_measured_train_ilc': int(missing6.detected_cells.gt(0).sum()),
+          'detection_rule': 'Native raw count > 0, with denominator restricted to per-shard measurement_mask = true',
+          'compressed_bytes_read': plan['compressed_bytes'], 'decoded_bytes_cumulative_upper_bound': plan['decoded_bytes_upper_bound'],
+          'extraction_seconds_before_grouped_summary': extraction['elapsed_seconds'],
+          'process_peak_rss_kib_before_grouped_summary': extraction['process_peak_rss_kib'],
+          'independent_direct_csr_rows': len(verification['direct_csr_rows']),
+          'original_h5ad_rows': len(verification['original_h5ad_rows']),
+          'original_source_replay_gaps': verification['original_source_gaps'],
+          'panel_membership_source': receipt(PLAN / 'ilc_targeted_audit_genes.csv'),
+          'final_panel_sources': [receipt(PLAN / 'panels' / size / 'selected_genes.csv') for size in ['7k', '8k']],
+          'donor_correction_source': receipt(geo),
+          'limitations': [
+              'Source ILC labels are inherited annotations, not independently established ILC biological identity.',
+              'No new classifier, relabelling, model generation, model evaluation, or panel ranking optimization was performed.',
+              'These cells belong to the baseline train source universe; genes outside 6k were not baseline 6k model targets.',
+              'Raw non-detection is finite native-depth sampling and cannot establish absent gene function or absent population.',
+              'Feature unavailable and measured zero are kept separate; the two CRLF2 loci are never merged.',
+              'Native depth varies across assays, donors and cohorts; counts are descriptive, with no downsampling or inferential test.',
+              'Only source-labelled ILC cells are selected, so rates do not estimate full-corpus or independently adjudicated ILC prevalence.',
+              'Donor correction for 300b follows existing corpus GEO rules; cross-dataset person duplication remains unresolved.',
+              'TRAC is absent from the canonical 20,097-gene source axis and was not represented as a measured zero.',
+              'Expression is an audit outcome only, not a basis for modifying 7k/8k membership or evidence priority.'
+          ],
+          'outputs': [receipt(p) for p in sorted(ROOT.iterdir()) if p.is_file() and p.name != 'RESULT.json']}
+dump('RESULT.json', result)
+print(json.dumps({k: result[k] for k in ['status', 'train_ilc_cells', 'datasets', 'corrected_dataset_scoped_donor_ids',
+      'target_raw_loci', 'missing6k_raw_loci', 'missing6k_loci_detected_in_at_least_one_measured_train_ilc']}))